-
Interpreting drug synergy in breast cancer with deep learning using target-protein inhibition profiles
- Back
Document Title
Interpreting drug synergy in breast cancer with deep learning using target-protein inhibition profiles
Author
Srithanyarat T. Taoma K. Sutthibutpong T. Ruengjitchatchawalya M. Liangruksa M. Laomettachit T.
Affiliations
Bioinformatics and Systems Biology Program School of Bioresources and Technology King Mongkut抯 University of Technology Thonburi Bangkok 10150 Thailand; School of Information Technology King Mongkut抯 University of Technology Thonburi Bangkok 10140 Thailand; Department of Physics Faculty of Science King Mongkut抯 University of Technology Thonburi Bangkok 10140 Thailand; Theoretical and Computational Physics Group Center of Excellence in Theoretical and Computational Science King Mongkut抯 University of Technology Thonburi Bangkok 10140 Thailand; Biotechnology Program School of Bioresources and Technology King Mongkut抯 University of Technology Thonburi Bangkok 10150 Thailand; National Nanotechnology Center (NANOTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani 12120 Thailand
Type
Article
Source Title
BioData Mining
ISSN
17560381
Year
2024
Volume
17
Issue
1
Open Access
All Open Access Gold
Publisher
BioMed Central Ltd
DOI
10.1186/s13040-024-00359-z
Abstract
Background: Breast cancer is the most common malignancy among women worldwide. Despite advances in treating breast cancer over the past decades drug resistance and adverse effects remain challenging. Recent therapeutic progress has shifted toward using drug combinations for better treatment efficiency. However with a growing number of potential small-molecule cancer inhibitors in silico strategies to predict pharmacological synergy before experimental trials are required to compensate for time and cost restrictions. Many deep learning models have been previously proposed to predict the synergistic effects of drug combinations with high performance. However these models heavily relied on a large number of drug chemical structural fingerprints as their main features which made model interpretation a challenge. Results: This study developed a deep neural network model that predicts synergy between small-molecule pairs based on their inhibitory activities against 13 selected key proteins. The synergy prediction model achieved a Pearson correlation coefficient between model predictions and experimental data of 0.63 across five breast cancer cell lines. BT-549 and MCF-7 achieved the highest correlation of 0.67 when considering individual cell lines. Despite achieving a moderate correlation compared to previous deep learning models our model offers a distinctive advantage in terms of interpretability. Using the inhibitory activities against key protein targets as the main features allowed a straightforward interpretation of the model since the individual features had direct biological meaning. By tracing the synergistic interactions of compounds through their target proteins we gained insights into the patterns our model recognized as indicative of synergistic effects. Conclusions: The framework employed in the present study lays the groundwork for future advancements especially in model interpretation. By combining deep learning techniques and target-specific models this study shed light on potential patterns of target-protein inhibition profiles that could be exploited in breast cancer treatment. ? The Author(s) 2024.
License
CC BY
Rights
Authors
Publication Source
WOS